The visualization of complex data

Analytical reasoning for real world problem solving involves large volumes of uncertain, complex, and often conflicting data that analysts need to make sense of. In this context, time-oriented data is commonplace and plays a special role. Due to the distinct characteristics of time, appropriate methods for exploration and analysis are needed.

For example when displaying lab results in electronic patient records this could mean to show expected value ranges for healthy patients depending on their context such as gender or age. For representing stock price data, the time axis would suppress weekends and bank holidays to avoid a distorted representation of value change. Solutions like these might be achieved by creating specialized applications for each domain and analysis problem at hand. However, this would cause lots of effort and make maintenance and reuse difficult.

To avoid this, Visual Analytics methods which adapt to different contexts and combine interactive visual interfaces with automated analysis methods will be designed. Even though computers have the ability to recognize and visualize patterns in data, they often lack the background knowledge to interpret said data. Therefore, human analysts and their expert knowledge are essential to the process of data analysis.

How to take advantage of human expert’s knowledge

Ideally, a Visual Analytics environment would adapt itself to the user’s context and domain specifics of the data to analyse by having access to human knowledge. The basic research project KAVA-Time explores how to take advantage of explicit expert knowledge in the Visual Analytics process to make analytical reasoning more effective and efficient. The project team develops and evaluates knowledge specification methods as well as knowledge-assisted visualization and interaction methods for time-oriented data. This encompasses two main objectives:

to capture analysts' domain knowledge and explorative interests

to take advantage of the explicit knowledge in interaction and visualization methods.The newly integrated specification methods will not only take into account externally given knowledge but also the reuse and sharing of these specifications. Interactive Visual Analytics methods will allow intuitive and direct refinement of explicit knowledge by analysts.

Insights gained by man and machine

Tackling this issue will give rise to more effective environments for gaining insights – the possibility to specify, model, and make use of auxiliary information about data and domain specifics in addition to the raw data, will help to better select, tailor, and adjust appropriate methods for visual representation, interaction, and automated analysis.